Correlation Between LGI Homes and Xponential Fitness
Can any of the company-specific risk be diversified away by investing in both LGI Homes and Xponential Fitness at the same time? Although using a correlation coefficient on its own may not help to predict future stock returns, this module helps to understand the diversifiable risk of combining LGI Homes and Xponential Fitness into the same portfolio, which is an essential part of the fundamental portfolio management process.
By analyzing existing cross correlation between LGI Homes and Xponential Fitness, you can compare the effects of market volatilities on LGI Homes and Xponential Fitness and check how they will diversify away market risk if combined in the same portfolio for a given time horizon. You can also utilize pair trading strategies of matching a long position in LGI Homes with a short position of Xponential Fitness. Check out your portfolio center. Please also check ongoing floating volatility patterns of LGI Homes and Xponential Fitness.
Diversification Opportunities for LGI Homes and Xponential Fitness
0.69 | Correlation Coefficient |
Poor diversification
The 3 months correlation between LGI and Xponential is 0.69. Overlapping area represents the amount of risk that can be diversified away by holding LGI Homes and Xponential Fitness in the same portfolio, assuming nothing else is changed. The correlation between historical prices or returns on Xponential Fitness and LGI Homes is a relative statistical measure of the degree to which these equity instruments tend to move together. The correlation coefficient measures the extent to which returns on LGI Homes are associated (or correlated) with Xponential Fitness. Values of the correlation coefficient range from -1 to +1, where. The correlation of zero (0) is possible when the price movement of Xponential Fitness has no effect on the direction of LGI Homes i.e., LGI Homes and Xponential Fitness go up and down completely randomly.
Pair Corralation between LGI Homes and Xponential Fitness
Given the investment horizon of 90 days LGI Homes is expected to generate 0.34 times more return on investment than Xponential Fitness. However, LGI Homes is 2.91 times less risky than Xponential Fitness. It trades about -0.16 of its potential returns per unit of risk. Xponential Fitness is currently generating about -0.06 per unit of risk. If you would invest 8,868 in LGI Homes on December 28, 2024 and sell it today you would lose (1,955) from holding LGI Homes or give up 22.05% of portfolio value over 90 days.
Time Period | 3 Months [change] |
Direction | Moves Together |
Strength | Significant |
Accuracy | 100.0% |
Values | Daily Returns |
LGI Homes vs. Xponential Fitness
Performance |
Timeline |
LGI Homes |
Xponential Fitness |
LGI Homes and Xponential Fitness Volatility Contrast
Predicted Return Density |
Returns |
Pair Trading with LGI Homes and Xponential Fitness
The main advantage of trading using opposite LGI Homes and Xponential Fitness positions is that it hedges away some unsystematic risk. Because of two separate transactions, even if LGI Homes position performs unexpectedly, Xponential Fitness can make up some of the losses. Pair trading also minimizes risk from directional movements in the market. For example, if an entire industry or sector drops because of unexpected headlines, the short position in Xponential Fitness will offset losses from the drop in Xponential Fitness' long position.LGI Homes vs. Arhaus Inc | LGI Homes vs. Floor Decor Holdings | LGI Homes vs. Haverty Furniture Companies | LGI Homes vs. Kirklands |
Xponential Fitness vs. Planet Fitness | Xponential Fitness vs. JAKKS Pacific | Xponential Fitness vs. Acushnet Holdings Corp | Xponential Fitness vs. OneSpaWorld Holdings |
Check out your portfolio center.Note that this page's information should be used as a complementary analysis to find the right mix of equity instruments to add to your existing portfolios or create a brand new portfolio. You can also try the Watchlist Optimization module to optimize watchlists to build efficient portfolios or rebalance existing positions based on the mean-variance optimization algorithm.
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